基于模糊的多目标调度,用于人机器人制造系统.
Yijun Deng1, Binrong Huang1, Shouliang Lai2
1College of Packaging Design and Art, Hunan University of Technology, Zhuzhou, 412000, Hunan, China.
Scientific reports
|February 27, 2026
概括
这项研究优化了使用模糊编程在制造业中的人机协作. 它平衡生产,库存和任务分配,以最大限度地提高价值并最大限度地减少延迟,即使需求不确定.
科学领域:
- 制造业 工程 制造工程
- 运营研究 运营研究
- 模糊系统 (Fuzzy Systems) 是一个模糊系统.
背景情况:
- 现代制造业面临着需求波动和人机交互的挑战.
- 优化生产规划和时间表对于效率和利能力至关重要.
研究的目的:
- 开发一个模糊的编程模型,同时优化生产,库存,人机器人任务分配和工作顺序.
- 在多期,多产品制造环境中解决需求和加工时间的不确定性.
主要方法:
- 为了处理不确定性,采用了悲观的 (可信度受限) 模糊编程方法.
- 对于小规模的问题使用了epsilon-constraint方法,而对更大的实例应用了元启发算法 (NSGA-II,MOPSO,MOWOA).
- 进行了灵敏度分析,以评估各种参数对目标功能的影响.
主要成果:
- 缩短完成时间会增加成本,降低净现值.
- 较高的不确定性率导致生产时间和短缺增加,净现值下降.
- 银行利率上4%显著降低了15.68%的净当前价值.
结论:
- MOWOA算法在产生人机协作高效解决方案的大规模问题上表现出卓越的性能.
- 该研究为优化在模糊制造环境中的综合生产规划和调度提供了实际见解.
- 有效地管理不确定性和财务参数对于最大限度地提高制造业价值至关重要.
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